Minor changes to avoid bugs in some compilers.

tools/python/src/decision_functions.cpp

@@ -122,7 +122,7 @@ std::vector<double> normalized_predict_vec (
 {
     std::vector<double> out;
     out.reserve(samps.size());
-    for (auto& x : samps)
+    for (const auto& x : samps)
         out.push_back(normalized_predict(df,x));
     return out;
 }

tools/python/src/face_recognition.cpp

@@ -64,10 +64,10 @@ public:
             throw dlib::error("The array of images and the array of array of locations must be of the same size");
 
         int total_chips = 0;
-        for (auto& faces : batch_faces)
+        for (const auto& faces : batch_faces)
         {
             total_chips += faces.size();
-            for (auto& f : faces)
+            for (const auto& f : faces)
             {
                 if (f.num_parts() != 68 && f.num_parts() != 5)
                     throw dlib::error("The full_object_detection must use the iBUG 300W 68 point face landmark style or dlib's 5 point style.");
@@ -82,7 +82,7 @@ public:
             auto& img = batch_imgs[i];
 
             std::vector<chip_details> dets;
-            for (auto& f : faces)
+            for (const auto& f : faces)
                 dets.push_back(get_face_chip_details(f, 150, 0.25));
             dlib::array<matrix<rgb_pixel>> this_img_face_chips;
             extract_image_chips(img, dets, this_img_face_chips);
@@ -214,12 +214,12 @@ void save_face_chips (
 
     int num_faces = faces.size();
     std::vector<chip_details> dets;
-    for (auto& f : faces)
+    for (const auto& f : faces)
         dets.push_back(get_face_chip_details(f, size, padding));
     dlib::array<matrix<rgb_pixel>> face_chips;
     extract_image_chips(numpy_image<rgb_pixel>(img), dets, face_chips);
     int i=0;
-    for (auto& chip : face_chips) 
+    for (const auto& chip : face_chips) 
     {
         i++;
         if(num_faces > 1) 

tools/python/src/global_optimization.cpp

@@ -203,7 +203,7 @@ std::shared_ptr<global_function_search> py_global_function_search1 (
 )
 {
     std::vector<function_spec> tmp;
-    for (auto i : functions)
+    for (const auto& i : functions)
         tmp.emplace_back(i.cast<function_spec>());
 
     return std::make_shared<global_function_search>(tmp);
@@ -216,14 +216,14 @@ std::shared_ptr<global_function_search> py_global_function_search2 (
 )
 {
     std::vector<function_spec> specs;
-    for (auto i : functions)
+    for (const auto& i : functions)
         specs.emplace_back(i.cast<function_spec>());
 
     std::vector<std::vector<function_evaluation>> func_evals;
-    for (auto i : initial_function_evals)
+    for (const auto& i : initial_function_evals)
     {
         std::vector<function_evaluation> evals;
-        for (auto j : i)
+        for (const auto& j : i)
         {
             evals.emplace_back(j.cast<function_evaluation>());
         }
@@ -410,12 +410,12 @@ simply a struct that records x and the scalar value F(x). )RAW")
             std::vector<std::vector<function_evaluation>> function_evals;
             self.get_function_evaluations(specs,function_evals); 
             py::list py_specs, py_func_evals;
-            for (auto& s : specs)
+            for (const auto& s : specs)
                 py_specs.append(s);
-            for (auto& i : function_evals)
+            for (const auto& i : function_evals)
             {
                 py::list tmp;
-                for (auto& j : i)
+                for (const auto& j : i)
                     tmp.append(j);
                 py_func_evals.append(tmp);
             }

tools/python/src/gui.cpp

@@ -97,7 +97,7 @@ void add_overlay_pylist (
 )
 {
     std::vector<rectangle> rects;
-    for (auto& obj : objs)
+    for (const auto& obj : objs)
     {
         try { rects.push_back(obj.cast<rectangle>()); continue; } catch(py::cast_error&) { }
         try { rects.push_back(obj.cast<drectangle>()); continue; } catch(py::cast_error&) { }

tools/python/src/image2.cpp

@@ -65,7 +65,7 @@ std::vector<point> py_remove_incoherent_edge_pixels (
     DLIB_CASSERT(num_rows(horz_gradient) == num_rows(vert_gradient));
     DLIB_CASSERT(num_columns(horz_gradient) == num_columns(vert_gradient));
     DLIB_CASSERT(angle_threshold >= 0);
-    for (auto& p : line)
+    for (const auto& p : line)
         DLIB_CASSERT(get_rect(horz_gradient).contains(p), "All line points must be inside the given images.");
 
     return remove_incoherent_edge_pixels(line, horz_gradient, vert_gradient, angle_threshold);
@@ -152,7 +152,7 @@ py::list py_extract_image_chips (
     dlib::array<numpy_image<T>> out;
     extract_image_chips(img, python_list_to_vector<chip_details>(chip_locations), out);
     py::list ret;
-    for (auto& i : out)
+    for (const auto& i : out)
         ret.append(i);
     return ret;
 }

tools/python/src/image4.cpp

@@ -511,7 +511,7 @@ py::list py_extract_image_chips (
     dlib::array<numpy_image<T>> out;
     extract_image_chips(img, python_list_to_vector<chip_details>(chip_locations), out);
     py::list ret;
-    for (auto& i : out)
+    for (const auto& i : out)
         ret.append(i);
     return ret;
 }

tools/python/src/image_dataset_metadata.cpp

@@ -97,7 +97,7 @@ dataset py_load_image_dataset_metadata(
 std::shared_ptr<std::map<std::string,point>> map_from_object(py::dict obj)
 {
     auto ret = std::make_shared<std::map<std::string,point>>();
-    for (auto& v : obj)
+    for (const auto& v : obj)
     {
         (*ret)[v.first.cast<std::string>()] = v.second.cast<point>();
     }
@@ -121,7 +121,7 @@ image_dataset_metadata::dataset py_make_bounding_box_regression_training_data (
         // otherwise, detections should be a list of std::vectors.
         py::list dets(detections);
         std::vector<std::vector<rectangle>> temp;
-        for (auto& d : dets)
+        for (const auto& d : dets)
             temp.emplace_back(d.cast<const std::vector<rectangle>&>());
         return make_bounding_box_regression_training_data(truth, temp);
     }
@@ -174,7 +174,7 @@ void bind_image_dataset_metadata(py::module &m_)
     auto partsstr = [](const std::map<std::string,point>& item) {
         std::ostringstream sout;
         sout << "{";
-        for (auto& v : item) 
+        for (const auto& v : item) 
             sout << "'" << v.first << "': " << v.second << ", ";
         sout << "}";
         return sout.str();
@@ -182,7 +182,7 @@ void bind_image_dataset_metadata(py::module &m_)
     auto partsrepr = [](const std::map<std::string,point>& item) {
         std::ostringstream sout;
         sout << "dlib.image_dataset_metadata.parts({\n";
-        for (auto& v : item) 
+        for (const auto& v : item) 
             sout << "'" << v.first << "': dlib.point" << v.second << ",\n";
         sout << "})";
         return sout.str();

tools/python/src/numpy_returns.cpp

@@ -97,12 +97,12 @@ py::list get_face_chips (
     py::list chips_list;
 
     std::vector<chip_details> dets;
-    for (auto& f : faces)
+    for (const auto& f : faces)
         dets.push_back(get_face_chip_details(f, size, padding));
     dlib::array<numpy_image<rgb_pixel>> face_chips;
     extract_image_chips(img, dets, face_chips);
 
-    for (auto& chip : face_chips) 
+    for (const auto& chip : face_chips) 
     {
         // Append image to chips list
         chips_list.append(chip);

tools/python/src/shape_predictor.cpp

@@ -72,7 +72,7 @@ std::shared_ptr<full_object_detection> full_obj_det_init(const rectangle& rect,
 {
     const unsigned long num_parts = py::len(pyparts);
     std::vector<point> parts;
-    for (auto& item : pyparts)
+    for (const auto& item : pyparts)
         parts.push_back(item.cast<point>());
 
     return std::make_shared<full_object_detection>(rect, parts);